Mars climate change research: Perspective of sulfur replacing carbon in martian sedimentary rocks

Object detection plays an important role in underground intelligent vehicles and intelligent transportation systems. Due to the uneven light in underground mining scenarios, infrared cameras are one of the typical onboard sensors for environmental perception. Although object detection has been studied for decades, it still confronts the challenge of detecting infrared objects in underground mines. The contributing factors include weak and small objects in infrared images and similar environments in mining scenarios. In this paper, a Feature Enhancement and Guided Network (FEGNet) is proposed to address these problems. Based on the characteristics of infrared images, the feature enhancement module (FEM) preserves the image details from global and local perspectives to improve the discrimination of weak and small objects. To tackle the problem of overfitting caused by similar environments, a receptive-field-guided (RFG) backbone is proposed to learn multi-scale context and spatial information. The experimental results on the underground mining (UM) dataset demonstrate that the mAP of the proposed FEGNet achieves 86.1%, which is 4.6% higher than the state-of-the-art CNN-based network YOLOv7.

MP05-16 THE DEVELOPMENT OF A THREE-DIMENSIONAL KIDNEY SPHEROID MODEL TO STUDY CALCIUM-BASED CRYSTAL FORMATION

Abstract The Mount Washington Observatory Regional Mesonet (MWRM) is a network of 18 remote meteorological monitoring stations (as of 2022), including the Auto Road Vertical Profile (ARVP), located across the White Mountains of northern New Hampshire. Each station measures temperature and relative humidity, with additional variables at many locations. All stations need to withstand the frequent combination of intense cold, high precipitation amounts, icing, and hurricane-force winds in a mountain environment. Due to these challenges, the MWRM employs rugged instrumentation, an innovative radio-communications relay approach, and carefully selected sites that balance ideal measuring environments with station survivability. Data collected from the MWRM are used operationally by forecasters (including Mount Washington Observatory and National Weather Service staff) to validate model guidance, by alpine and climate scientists, by recreationalists accessing conditions in the backcountry, by groups operating on the mountain (Cog Railway, toll Auto Road), and by search and rescue organizations. This paper provides a detailed description of the network, with emphasis on how the challenging climate and terrain of this mountain region impacts sensor selection, site maintenance, and overall operation. Significance Statement The mountain environment is a heterogeneous landscape, and interactions between the atmosphere and terrain can cause a wide variety of conditions across time and space. Our network of remote stations at different elevations across the White Mountains allows data users to understand how the weather varies spatially across the mountain range where conditions on higher peaks can be drastically, and dangerously, different. Sharing information about the MWRM can help other groups establish networks in similar challenging environments, and broaden our understanding of weather and climate in mountainous regions.

Environmental and climatic drivers of phenotypic evolution and distribution changes in a widely distributed subfamily of subterranean mammals

Although immunotherapy is becoming a standard approach of human cancer treatment, only a small but critical fraction of patients responds to the therapy. It is therefore required to determine the sub-populations of patients who will respond to immunotherapies along with developing novel strategies to improve efficacy of anti-tumor immune reactions. Current development of novel immunotherapies relies heavily on mouse models of cancer. These models are important for better understanding of mechanisms behind tumor immune escape and investigation of novel strategies to overcome it. Nevertheless, the murine models do not necessarily represent the complexity of spontaneously occurring cancers in humans. Dogs spontaneously develop a wide range of cancer types with an intact immune system under similar environment and exposure to humans, which can serve as translational models in cancer immunotherapy research. To date though, there is still a relatively limited amount of information regarding immune cell profiles in canine cancers. One possible reason could be that there are hardly any established methods to isolate and simultaneously detect a range of immune cell types in neoplastic tissues. To date only a single manuscript describes characterization of immune cells in canine tumour tissues, concentrating solely on T-cells. Here we describe a protocol for multi-color flow cytometry to distinguish immune cell types in blood, lymph nodes, and neoplastic tissues from dogs with cancer. Our results demonstrate that a 9-color flow cytometry panel enables characterization of different cell subpopulations including myeloid cells. We also show that the panel allows detection of minor/aberrant subsets within a mixed population of cells in various neoplastic samples including blood, lymph node and solid tumors. To our knowledge, this is the first simultaneous immune cell detection panel applicable for solid tumors in dogs. This multi-color flow cytometry panel has the potential to inform future basic research focusing on immune cell functions in translational canine cancer models.

The characterisation of ecological strategies to predict drought response is still lacking for Caribbean dry forest seedlings. This study documents growth characteristics and tolerance to drought via xylem hydraulic and leaf cell properties of three dominant native species of the Caribbean dry forest. Twenty morphological and physiological traits were assessed in Citharexylum spinosum, Guaiacum officinale and Guapira fragrans in greenhouse conditions. The seedlings displayed contrasting growth rates, which were positively correlated with the capacity to quickly develop a large leaf area and root fraction. The three species had a similar xylem tolerance to embolism (P50: −4 MPa) but differed in leaf cell tolerance to dehydration, which was negatively correlated with RGR (R2 > 0.87). The slowest-growing, G. officinale, had high leaf tolerance to cell dehydration due to low ΨTLP and πo, but displayed a narrow hydraulic safety margin. The leaves of the fast-growing C. spinosum were sensitive to leaf dehydration but exhibited a surprisingly wide stem hydraulic safety margin. G. fragrans had intermediate traits. Our results showed that dry forest seedling growth in similar environments can exhibit distinct carbon growth strategies as well as contrasting water-use strategies, primarily as they relate to drought resistance, due to variation in root development and leaf cell resistance to dehydration. Our study thus provides an approach to estimate species performance under drought conditions.

The spread of nonindigenous species by shipping is a large and growing global problem that harms coastal ecosystems and economies and may blur coastal biogeographical patterns. This study coupled eukaryotic environmental DNA (eDNA) metabarcoding with dissimilarity regression to test the hypothesis that ship-borne species spread homogenizes port communities. We first collected and metabarcoded water samples from ports in Europe, Asia, Australia and the Americas. We then calculated community dissimilarities between port pairs and tested for effects of environmental dissimilarity, biogeographical region and four alternative measures of ship-borne species transport risk. We predicted that higher shipping between ports would decrease community dissimilarity, that the effect of shipping would be small compared to that of environment dissimilarity and shared biogeography, and that more complex shipping risk metrics (which account for ballast water and stepping-stone spread) would perform better. Consistent with our hypotheses, community dissimilarities increased significantly with environmental dissimilarity and, to a lesser extent, decreased with ship-borne species transport risks, particularly if the ports had similar environments and stepping-stone risks were considered. Unexpectedly, we found no clear effect of shared biogeography, and that risk metrics incorporating estimates of ballast discharge did not offer more explanatory power than simpler traffic-based risks. Overall, we found that shipping homogenizes eukaryotic communities between ports in predictable ways, which could inform improvements in invasive species policy and management. We demonstrated the usefulness of eDNA metabarcoding and dissimilarity regression for disentangling the drivers of large-scale biodiversity patterns. We conclude by outlining logistical considerations and recommendations for future studies using this approach.

Satellite radar altimetry has been widely utilized in hydrological research, particularly with the advent of Sentinel-3, a Synthetic Aperture Radar (SAR) altimeter operating globally and equipped with an innovative onboard tracking system referred to as the open-loop tracking command (OLTC). Utilizing a pseudo-DEM (Digital Elevation Model), controlled through the OLTC, holds significant promise for the reliable observation of inland water bodies. Nevertheless, the complex geographical conditions in high mountain and reservoir river basins pose challenges in defining an appropriate pseudo-DEM for hydrological targets, potentially leading to reduced performance of Sentinel-3. This study aims to comprehensively evaluate the performance of Sentinel-3 by selecting the Lancang and Nu River basins in southwest China as a case study. These two rivers have a similar natural environment, but cascade reservoirs distinguish the Lancang River basin. By analyzing waveform energy from echoes of virtual stations (VSs) in both river basins (27 VSs in the Lancang River basin and 39 VSs in the Nu River basin), the performance of Sentinel-3 in different tracking modes and OLTC versions were compared. The results indicated that the detection rate of Sentinel-3A increased when transitioning from the closed-loop mode to the open-loop mode and with the implementation of newer OLTC versions (36.8% increased to 47.4%, 60.5%, and 63.2% in OLTC V5.0, V6.0, and V6.1, respectively). Similarly, the detection rate of Sentinel-3B rose from 64.3% (OLTC V2.0) to 71.4% and 75.0% in OLTC V3.0 and V3.1, respectively. Additionally, the cascade reservoir causing river channel expansion results in a better performance of Sentinel-3A in the Lancang River compared to the Nu River in the closed-loop mode (13.0% and 35.7%, respectively). Nevertheless, the considerable fluctuations in water surface caused by reservoir impoundment led to a wrong pseudo-DEM, resulting in poor performance of Sentinel-3 in reservoir regions before OLTC V6.0 was updated. The detection rate of low altitude, broad water surfaces (>500 m) decreased from 100% in a closed-loop mode to 0% in an open-loop mode, but increased to 100% in OLTC V6.0 and V6.1, respectively. The detection rate of high altitude, narrow water surfaces (<500 m) increased from 0% in a closed-loop mode to 40.9% in OLTC V6.1. Although the detection ability of Sentinel-3 is improving with the implementation of newer OLTC versions, the seasonal variations (usually more than 60 m) of water levels in reservoirs exceeded the size of the range window (60 m), rendering a complete measurement impossible.

Spatial inconsistency of memorized positions produces different types of movements

ABSTRACT The abiotic and biotic conditions in forest ecosystems can be significantly influenced by forest fires. However, difficulties in policy decisions for restoration inevitably occur in the absence of information on the damaged forests, such as location, area, and burn severity. In this study, eight spectral indices calculated from Sentinel 2 MSI imagery and machine learning algorithms (Random Forest (RF) and Support Vector Machine (SVM)) were used for mapping burned areas and severity. Two study sites with similar meteorological environment (dry season) and species (coniferous vegetation) were tested, and dataset (EMSR448) from Copernicus Emergency Management Service (CEMS) was used as the reference truth. RF showed better performance for classifying pixels from classes with similar properties than SVM. Normalized Burn Ratio (NBR) and Green Normalized Difference Vegetation Index (GNDVI) showed high importance in assessing fire severity suggesting that it may be effective for identifying senescent plants. The results also confirmed that the CEMS dataset has transferability as a reference truth for fire damage classification in other regions. Implementation of this method enables fast and accurate mapping of the area and severity of destructive damage by forest fires, and also has applicability for other disasters.

The efficient and selective functionalization of icosahedral carboranes (C2B10H12) at the boron vertexes is a long-standing challenge owing to the presence of 10 inert B-H bonds in a similar chemical environment. Herein, we report a new reaction paradigm for direct B-H functionalization of icosahedral carboranes via B-H homolysis enabled by a nitrogen-centered radical-mediated hydrogen atom transfer (HAT) strategy. Both the HAT process of the carborane B-H bond and the resulting boron-centered carboranyl radical intermediate have been confirmed experimentally. The reaction occurs at the most electron-rich boron vertex with the lowest B-H bond dissociation energy (BDE). Using this strategy, diverse carborane derivatization, including thiolation, selenation, alkynylation, alkenylation, cyanation, and halogenation, have been achieved in satisfactory yields under a photoinitiated condition in a metal-free and redox-neutral fashion. Moreover, the synthetic utility of the current protocol was also demonstrated by both the scale-up reaction and the construction of carborane-based functional molecules. Therefore, this methodology opens a radical pathway to carborane functionalization, which is distinct from the B-H heterolytic mechanism in the traditional strategies.

Obtaining and managing groundwater data is difficult as it is common for time series datasets representing groundwater levels at wells to have large gaps of missing data. To address this issue, many methods have been developed to infill or impute the missing data. We present a method for improving data imputation through an iterative refinement model (IRM) machine learning framework that works on any aquifer dataset where each well has a complete record that can be a mixture of measured and input values. This approach corrects the imputed values by using both in situ observations and imputed values from nearby wells. We relied on the idea that similar wells that experience a similar environment (e.g., climate and pumping patterns) exhibit similar changes in groundwater levels. Based on this idea, we revisited the data from every well in the aquifer and “re-imputed” the missing values (i.e., values that had been previously imputed) using both in situ and imputed data from similar, nearby wells. We repeated this process for a predetermined number of iterations—updating the well values synchronously. Using IRM in conjuncture with satellite-based imputation provided better imputation and generated data that could provide valuable insight into aquifer behavior, even when limited or no data were available at individual wells. We applied our method to the Beryl-Enterprise aquifer in Utah, where many wells had large data gaps. We found patterns related to agricultural drawdown and long-term drying, as well as potential evidence for multiple previously unknown aquifers.

Most empirical equations used for wave runup predictions have been developed from measurements at straight sandy beaches in unlimited fetch environments. While there are empirical equations to predict wave runup on gravel beaches, they have not been tested for prediction of wave runup on pocket gravel beaches, in limited-fetch environment, which can be found around Mediterranean. This paper addresses this lack of measurements on this type of beaches and examines the alongshore variability of wave runup. Wave runup measurements were made using video observations along 3 cross-sectional profiles on the pocket beach of Ploče, Croatia. The measurements have shown that the wave runup can vary for about 71% even around the centerline of the pocket beach. This variability is due to beach orientation and alignment of beach profiles to the prevailing wave direction, as well as difference in beach slope. Comparison of wave runup predictions from five well-known empirical equations and field measurements showed significant underprediction (up to NBIAS = −0.33) for energetic wave events, and overall high scatter (up to NRMSE = 0.38). The best performing wave runup equation was used for further refinement outside the original parameter space by including the Goda wave peakedness parameter (Qp). The newly developed empirical equation for wave runup reduced the NBIAS to 0 and the NRMSE by 31% compared to the original equation (developed equation metrics: R = 0.91, NBIAS = 0, NRMSE = 0.2, HH = 0.2 on the study site). This empirical equation can potentially be used for design of coastal structures and artificial beaches in similar environments, but further measurements are needed to test its applicability to a range of forcing and environmental conditions.

Calculation and modeling of 232Th distribution in Gorgan Bay in the Caspian Sea

We investigated the physical properties and tooth-whitening effect of polymeric tooth-whitening compositions based on orally acceptable polymers, polyvinyl acetate (PVAc), ethyl cellulose (EC), and polyvinyl pyrrolidone. The tooth-whitening composition was prepared with hydrogen peroxide (H2O2) as a tooth-bleaching agent and an orally acceptable polymer through simple mixing and stirring in ethyl alcohol. PVAc and EC polymers showed non-erosive features and sustainable polymeric matrices in a similar oral environment. In particular, non-erosive PVAc polymer exhibited excellent adhesive and flexible film matrix on bovine teeth. PVAc-H2O2 tooth-whitening composition presented a residual H2O2 and an overall color change value (ΔE*) of 26.5% and 16.54%, respectively. The non-erosive polymeric composition is expected to improve tooth-whitening efficacy in various oral products.

BackgroundVarious environmental risk factors have been associated with the pathogenesis of inflammatory bowel disease. In this study we aimed to identify lifestyle factors that affect the onset of Crohn’s disease and ulcerative colitis.Methods2294 patients from the Swiss IBD Cohort Study received a questionnaire regarding physical activity, nutritional habits and status of weight. In addition, a control group was formed comprising patients’ childhood friends, who grew up in a similar environment. ResultsOverall, 1111 questionnaires were returned (response rate: 48.4%). Significantly more patients with inflammatory bowel disease reported no regular practice of sport during childhood and beginning of adulthood compared to the control group (p = 0.0001). No association between intake of refined sugar and onset of inflammatory bowel disease was observed. More patients with Crohn’s disease compared to ulcerative colitis and controls suffered from overweight during childhood (12.8% vs. 7.7% and 9.7%, respectively; p = 0.027).ConclusionsOur study underlines the relevance of environmental factors in the development of inflammatory bowel disease. Our results imply a protective effect of physical activity regarding the onset of inflammatory bowel disease.

Political power shift in host markets and firm asset retrenchment: Evidence from Chinese MNCs

Semantic mapping can help robots better understand the environment and is extensively studied in robotics. However, it is a challenge for semantic mapping that calibrates all the obstacles with semantics. We propose integrating two network models to realize the salient semantic segmentation used for mobile robot mapping, which differs from traditional segmentation methods. Firstly, we detected salient objects. The detection result was the grayscale image form, which was recognized and annotated by our trained model. Then, we projected the salient objects’ contour with semantics to the corresponding RGB image, which realized the salient objects’ semantic segmentation. We treated the salient objects instead of all the obstacles as semantic segmentation objects that could reduce the background consideration. The neural network model trained based on the salient object’s shape information was stable for object recognition and easy for model training. We only used the shape feature for training, which could reduce the calculation amount of feature details. Experiments demonstrated that the algorithm could quickly realize the model’s training and provide a semantic landmark in the point cloud map as the relative position reference for robot repositioning when the map needs to be used again and exist in a similar environment.

Estimating the runoff response from hillslopes treated with soil and water conservation structures in the semi-arid Ethiopian highlands: Is the curve number method applicable?